Gene-based Pathogen Identification
microbiome-pathogen-detection-from-nanopore-foodborne-data/gene-based-pathogen-identification
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flowchart TD 0["ℹ️ Input Collection\ncollection_of_preprocessed_samples"]; style 0 stroke:#2c3143,stroke-width:4px; 1["Extract element identifiers"]; 0 -->|output| 1; d82a93c6-23a9-4f85-879c-ecd759a31087["Output\nextracted_samples_IDs"]; 1 --> d82a93c6-23a9-4f85-879c-ecd759a31087; style d82a93c6-23a9-4f85-879c-ecd759a31087 stroke:#2c3143,stroke-width:4px; 2["Build list"]; 0 -->|output| 2; f5b5b256-8ddf-4da8-8111-b1f6d3025a0d["Output\nlist_of_lists_of_preprocessed_samples"]; 2 --> f5b5b256-8ddf-4da8-8111-b1f6d3025a0d; style f5b5b256-8ddf-4da8-8111-b1f6d3025a0d stroke:#2c3143,stroke-width:4px; 3["Split file"]; 1 -->|output| 3; eb5317bd-4bad-4cad-9219-3ac379221d6e["Output\nsplitted_extracted_samples_IDs"]; 3 --> eb5317bd-4bad-4cad-9219-3ac379221d6e; style eb5317bd-4bad-4cad-9219-3ac379221d6e stroke:#2c3143,stroke-width:4px; 4["Flye"]; 2 -->|output| 4; ff0d8c7b-2ac7-4c6d-a110-f71418dad938["Output\nflye_consensus_fasta"]; 4 --> ff0d8c7b-2ac7-4c6d-a110-f71418dad938; style ff0d8c7b-2ac7-4c6d-a110-f71418dad938 stroke:#2c3143,stroke-width:4px; 2385436f-fbe8-4d77-a40b-27a097d02941["Output\nflye_assembly_graph"]; 4 --> 2385436f-fbe8-4d77-a40b-27a097d02941; style 2385436f-fbe8-4d77-a40b-27a097d02941 stroke:#2c3143,stroke-width:4px; 0e42ce51-c46b-45d8-baa4-45c0e98ac712["Output\nflye_assembly_gfa"]; 4 --> 0e42ce51-c46b-45d8-baa4-45c0e98ac712; style 0e42ce51-c46b-45d8-baa4-45c0e98ac712 stroke:#2c3143,stroke-width:4px; 3f297aed-7cee-4999-bbf4-69d84de6b64f["Output\nflye_assembly_info_tabular"]; 4 --> 3f297aed-7cee-4999-bbf4-69d84de6b64f; style 3f297aed-7cee-4999-bbf4-69d84de6b64f stroke:#2c3143,stroke-width:4px; 5["Parse parameter value"]; 3 -->|list_output_txt| 5; 3608e170-c462-42a2-8003-f3f65baa3834["Output\nparsed_extracted_samples_IDs_to_text"]; 5 --> 3608e170-c462-42a2-8003-f3f65baa3834; style 3608e170-c462-42a2-8003-f3f65baa3834 stroke:#2c3143,stroke-width:4px; 6["medaka consensus pipeline"]; 4 -->|consensus| 6; 0 -->|output| 6; 9e5ad6ec-b408-4132-ba07-dec9fa626923["Output\nmedaka_gaps_in_draft_bed_file"]; 6 --> 9e5ad6ec-b408-4132-ba07-dec9fa626923; style 9e5ad6ec-b408-4132-ba07-dec9fa626923 stroke:#2c3143,stroke-width:4px; fcbd3e3f-2e93-4798-b696-dad7db9f2efd["Output\nmedaka_log_file"]; 6 --> fcbd3e3f-2e93-4798-b696-dad7db9f2efd; style fcbd3e3f-2e93-4798-b696-dad7db9f2efd stroke:#2c3143,stroke-width:4px; 60656aac-ad2a-4c9b-9a68-b9fb18ae5595["Output\nmedaka_propability_h5_file"]; 6 --> 60656aac-ad2a-4c9b-9a68-b9fb18ae5595; style 60656aac-ad2a-4c9b-9a68-b9fb18ae5595 stroke:#2c3143,stroke-width:4px; c790d434-8e78-4df0-a0d4-8f9da0692158["Output\nmedaka_calls_of_draft_bam_file"]; 6 --> c790d434-8e78-4df0-a0d4-8f9da0692158; style c790d434-8e78-4df0-a0d4-8f9da0692158 stroke:#2c3143,stroke-width:4px; df361e19-b6d1-405b-96cc-b48c1ab7c604["Output\nsample_all_contigs"]; 6 --> df361e19-b6d1-405b-96cc-b48c1ab7c604; style df361e19-b6d1-405b-96cc-b48c1ab7c604 stroke:#2c3143,stroke-width:4px; 7["Bandage Image"]; 4 -->|assembly_gfa| 7; 9612a851-1f94-4d54-b001-5d082bcc9055["Output\nbandage_assembly_graph_image"]; 7 --> 9612a851-1f94-4d54-b001-5d082bcc9055; style 9612a851-1f94-4d54-b001-5d082bcc9055 stroke:#2c3143,stroke-width:4px; 8["Compose text parameter value"]; 5 -->|text_param| 8; 9["FASTA-to-Tabular"]; 6 -->|out_consensus| 9; de06c362-5b0a-4c29-956e-fbf02539789a["Output\nsample_specific_contigs_tabular_file_preparation"]; 9 --> de06c362-5b0a-4c29-956e-fbf02539789a; style de06c362-5b0a-4c29-956e-fbf02539789a stroke:#2c3143,stroke-width:4px; 10["ABRicate"]; 6 -->|out_consensus| 10; 8f227fc7-2d92-4c6f-af64-e841c1315b4f["Output\nabricate_with_vfdb_to_identify_genes_with_VFs"]; 10 --> 8f227fc7-2d92-4c6f-af64-e841c1315b4f; style 8f227fc7-2d92-4c6f-af64-e841c1315b4f stroke:#2c3143,stroke-width:4px; 11["ABRicate"]; 6 -->|out_consensus| 11; fa021d98-d885-4834-ac62-f30d5792260e["Output\nabricate_report_using_ncbi_database_to_indentify_amr"]; 11 --> fa021d98-d885-4834-ac62-f30d5792260e; style fa021d98-d885-4834-ac62-f30d5792260e stroke:#2c3143,stroke-width:4px; 12["Replace"]; 8 -->|out1| 12; 9 -->|output| 12; 174cb2c3-ed28-453c-8afb-85150e0b51ad["Output\nsample_specific_contigs_tabular_file"]; 12 --> 174cb2c3-ed28-453c-8afb-85150e0b51ad; style 174cb2c3-ed28-453c-8afb-85150e0b51ad stroke:#2c3143,stroke-width:4px; 13["Replace"]; 8 -->|out1| 13; 10 -->|report| 13; 19e56e00-6eef-4a4e-9cc6-d93dbf9420ad["Output\nvfs"]; 13 --> 19e56e00-6eef-4a4e-9cc6-d93dbf9420ad; style 19e56e00-6eef-4a4e-9cc6-d93dbf9420ad stroke:#2c3143,stroke-width:4px; 14["Replace"]; 8 -->|out1| 14; 11 -->|report| 14; a8eb74a5-362b-4f0f-a944-672e27981a41["Output\namrs"]; 14 --> a8eb74a5-362b-4f0f-a944-672e27981a41; style a8eb74a5-362b-4f0f-a944-672e27981a41 stroke:#2c3143,stroke-width:4px; 15["Tabular-to-FASTA"]; 12 -->|outfile| 15; a5bb1cbe-1bde-49a1-ad86-6d57b1319fbe["Output\ncontigs"]; 15 --> a5bb1cbe-1bde-49a1-ad86-6d57b1319fbe; style a5bb1cbe-1bde-49a1-ad86-6d57b1319fbe stroke:#2c3143,stroke-width:4px;
Inputs
Input | Label |
---|---|
Input dataset collection | collection_of_preprocessed_samples |
Outputs
From | Output | Label |
---|---|---|
toolshed.g2.bx.psu.edu/repos/iuc/collection_element_identifiers/collection_element_identifiers/0.0.2 | Extract element identifiers | |
__BUILD_LIST__ | Build list | |
toolshed.g2.bx.psu.edu/repos/bgruening/split_file_to_collection/split_file_to_collection/0.5.0 | Split file | |
toolshed.g2.bx.psu.edu/repos/bgruening/flye/flye/2.9.1+galaxy0 | Flye | |
param_value_from_file | Parse parameter value | |
toolshed.g2.bx.psu.edu/repos/iuc/medaka_consensus_pipeline/medaka_consensus_pipeline/1.7.2+galaxy0 | medaka consensus pipeline | |
toolshed.g2.bx.psu.edu/repos/iuc/bandage/bandage_image/2022.09+galaxy4 | Bandage Image | |
toolshed.g2.bx.psu.edu/repos/devteam/fasta_to_tabular/fasta2tab/1.1.1 | FASTA-to-Tabular | |
toolshed.g2.bx.psu.edu/repos/iuc/abricate/abricate/1.0.1 | ABRicate | |
toolshed.g2.bx.psu.edu/repos/iuc/abricate/abricate/1.0.1 | ABRicate | |
toolshed.g2.bx.psu.edu/repos/bgruening/text_processing/tp_find_and_replace/1.1.4 | Replace | |
toolshed.g2.bx.psu.edu/repos/bgruening/text_processing/tp_find_and_replace/1.1.4 | Replace | |
toolshed.g2.bx.psu.edu/repos/bgruening/text_processing/tp_find_and_replace/1.1.4 | Replace | |
toolshed.g2.bx.psu.edu/repos/devteam/tabular_to_fasta/tab2fasta/1.1.1 | Tabular-to-FASTA |
Tools
To use these workflows in Galaxy you can either click the links to download the workflows, or you can right-click and copy the link to the workflow which can be used in the Galaxy form to import workflows.
Importing into Galaxy
Below are the instructions for importing these workflows directly into your Galaxy server of choice to start using them!Hands-on: Importing a workflow
- Click on Workflow on the top menu bar of Galaxy. You will see a list of all your workflows.
- Click on galaxy-upload Import at the top-right of the screen
- Provide your workflow
- Option 1: Paste the URL of the workflow into the box labelled “Archived Workflow URL”
- Option 2: Upload the workflow file in the box labelled “Archived Workflow File”
- Click the Import workflow button
Below is a short video demonstrating how to import a workflow from GitHub using this procedure:
Video: Importing a workflow from URL
Version History
Version | Commit | Time | Comments |
---|---|---|---|
2 | cdd93376a | 2024-06-06 12:00:29 | adding tags to some of the workflow outputs, updating the training with the latest PathoGFAIR workflows updates |
1 | c63ce23c7 | 2024-05-26 12:29:47 | updating workflows file names |
For Admins
Installing the workflow tools
wget https://training.galaxyproject.org/training-material/topics/microbiome/tutorials/pathogen-detection-from-nanopore-foodborne-data/workflows/gene_based_pathogen_identification.ga -O workflow.ga workflow-to-tools -w workflow.ga -o tools.yaml shed-tools install -g GALAXY -a API_KEY -t tools.yaml workflow-install -g GALAXY -a API_KEY -w workflow.ga --publish-workflows